Robit regression in Stata

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Abstract

Logistic and probit models are the most popular regression model for binary
outcomes. A simple robust alternative is the robit model, which replaces the
underlying Normal distribution in the probit model with a Student t–distribution.
The heavier tails of the t–distribution (compared with the Normal distribution)
means that model outliers are less influential. Robit regression can be fit as a
generalized linear model with the link function defined as the inverse cumulative t–
distribution function with a specified number of degrees of freedom (df), and it has
been advocated as being particularly suitable for estimating inverse–probability
weights and propensity scoring more generally. Here we describe a new package
called robit that implements robit regression in Stata.
Original languageEnglish
JournalSTATA JOURNAL
Publication statusAccepted/In press - 1 Nov 2022

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